Intent-based dynamic change of region of interest of vehicle perception system

ABSTRACT

The present disclosure provides a perception system for a vehicle. The perception system includes a number of imaging devices associated with the vehicle and a perception filter for determining a region of interest (“ROI”) for the vehicle based on an intent of the vehicle and a current state of the vehicle. The ROI is used to select images of an environment of the vehicle produced by the imaging devices and the perception filter receives and processes the images produced by the imaging device. The perception system further includes a perception module for receiving the processed images from the perception filter and perceiving the environment of the vehicle based at least in part on the received images.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of and priority from U.S. patentapplication Ser. No. 16/586,470, filed Sep. 27, 2019, entitled“INTENT-BASED DYNAMIC CHANGE OF RESOLUTION AND REGION OF INTEREST OFVEHICLE PERCEPTION SYSTEM,” incorporated herein by reference in itsentirety.

TECHNICAL FIELD OF THE DISCLOSURE

The present disclosure relates generally to autonomous vehicles (AVs)and, more specifically, to devices and methods for intent-based dynamicchange of resolution, region of interest (ROI), and compute resources ofa perception system for such vehicles.

BACKGROUND

Accurately and quickly perceiving an autonomous vehicle's environmentand surroundings is of the utmost importance for the vehicle; however,these two goals are often in direct opposition to one another. Forexample, high-resolution imagers, such as computer vision (CV) devices,cameras, and LIDAR devices, provide large amounts of image data that maybe used to perceive an autonomous vehicle's surroundings accurately byenabling precise and reliable detection; however, the volume ofinformation provided by such imagers increases the overall latency ofthe perception system, thereby decreasing the reaction time that may beachieved by the autonomous vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

To provide a more complete understanding of the present disclosure andfeatures and advantages thereof, reference is made to the followingdescription, taken in conjunction with the accompanying figures, whereinlike reference numerals represent like parts, in which:

FIG. 1 is a block diagram illustrating an example autonomous vehicle inwhich a perception system according to some embodiments of the presentdisclosure may be implemented;

FIG. 2 is a block diagram illustrating an example perception systemaccording to some embodiments of the present disclosure;

FIG. 3 is a flowchart of an example method implemented by an exampleperception system according to some embodiments of the presentdisclosure; and

FIGS. 4A-4D illustrate example use cases of an example perception systemaccording to some embodiments of the present disclosure.

DESCRIPTION OF EXAMPLE EMBODIMENTS OF THE DISCLOSURE

The systems, methods and devices of this disclosure each have severalinnovative aspects, no single one of which is solely responsible for theall of the desirable attributes disclosed herein. Details of one or moreimplementations of the subject matter described in this specificationare set forth in the description below and the accompanying drawings.

Embodiments of the present disclosure provide a perception system for avehicle. The perception system includes a number of imaging devicesassociated with the vehicle and a perception filter for determining aregion of interest (“ROI”) for the vehicle based on an intent of thevehicle and a current state of the vehicle. The ROI is used to selectimages of an environment of the vehicle produced by the imaging devicesand the perception filter receives and processes the images produced bythe imaging device. The perception system further includes a perceptionmodule for receiving the processed images from the perception filter andperceiving the environment of the vehicle based at least in part on thereceived images.

Embodiments of the present disclosure also provide an autonomous vehicle(“AV”) including an onboard computer, a sensor suite comprising aplurality of imaging devices, and a perception system. The perceptionsystem includes a plurality of imaging devices for producing images ofan environment of the AV and a perception filter for determining aregion of interest (“ROI”) for the vehicle based on an intent of thevehicle and a current state of the vehicle. The ROI is used to selectimages of an environment of the vehicle produced by the imaging devicesand the perception filter receives and processes the images produced bythe imaging devices. The perception system further includes a perceptionmodule for receiving the processed images from the perception filter andperceiving the environment of the vehicle based at least in part on thereceived images.

Embodiments of the present disclosure still further provide a methodincluding deploying a plurality of imaging devices for producing imagesof an environment of a vehicle and determining by a region of interest(“ROI”) for the vehicle based on an intent of the vehicle and a currentstate of the vehicle. The ROI is used to select images of an environmentof the vehicle produced by the imaging devices and the perception filterreceives and processes the images produced by the imaging devices. Themethod further includes receiving by a perception module the processedimages from the perception filter and perceiving the environment of thevehicle based at least in part on the received images.

Embodiments disclosed herein may be particularly advantageous fordynamically changing the resolution, ROI, and compute resources of aperception system for an autonomous vehicle based on the intent of theautonomous vehicle and the temporal and situational priorities of theautonomous vehicle.

As will be appreciated by one skilled in the art, aspects of the presentdisclosure, in particular aspects of a perception system for anautonomous vehicle, described herein, may be embodied in various manners(e.g., as a method, a system, a computer program product, or acomputer-readable storage medium). Accordingly, aspects of the presentdisclosure may take the form of an entirely hardware embodiment, anentirely software embodiment (including firmware, resident software,micro-code, etc.) or an embodiment combining software and hardwareaspects that may all generally be referred to herein as a “circuit,”“module” or “system.” Functions described in this disclosure may beimplemented as an algorithm executed by one or more hardware processingunits, e.g. one or more microprocessors, of one or more computers. Invarious embodiments, different steps and portions of the steps of eachof the methods described herein may be performed by different processingunits. Furthermore, aspects of the present disclosure may take the formof a computer program product embodied in one or more computer readablemedium(s), preferably non-transitory, having computer readable programcode embodied, e.g., stored, thereon. In various embodiments, such acomputer program may, for example, be downloaded (updated) to theexisting devices and systems (e.g. to the existing perception systemdevices and/or their controllers, etc.) or be stored upon manufacturingof these devices and systems.

The following detailed description presents various descriptions ofspecific certain embodiments. However, the innovations described hereincan be embodied in a multitude of different ways, for example, asdefined and covered by the claims and/or select examples. In thefollowing description, reference is made to the drawings, in which likereference numerals can indicate identical or functionally similarelements. It will be understood that elements illustrated in thedrawings are not necessarily drawn to scale. Moreover, it will beunderstood that certain embodiments can include more elements thanillustrated in a drawing and/or a subset of the elements illustrated ina drawing. Further, some embodiments can incorporate any suitablecombination of features from two or more drawings.

The following disclosure describes various illustrative embodiments andexamples for implementing the features and functionality of the presentdisclosure. While particular components, arrangements, and/or featuresare described below in connection with various example embodiments,these are merely examples used to simplify the present disclosure andare not intended to be limiting. It will of course be appreciated thatin the development of any actual embodiment, numerousimplementation-specific decisions must be made to achieve thedeveloper's specific goals, including compliance with system, business,and/or legal constraints, which may vary from one implementation toanother. Moreover, it will be appreciated that, while such a developmenteffort might be complex and time-consuming; it would nevertheless be aroutine undertaking for those of ordinary skill in the art having thebenefit of this disclosure.

In the Specification, reference may be made to the spatial relationshipsbetween various components and to the spatial orientation of variousaspects of components as depicted in the attached drawings. However, aswill be recognized by those skilled in the art after a complete readingof the present disclosure, the devices, components, members,apparatuses, etc. described herein may be positioned in any desiredorientation. Thus, the use of terms such as “above”, “below”, “upper”,“lower”, “top”, “bottom”, or other similar terms to describe a spatialrelationship between various components or to describe the spatialorientation of aspects of such components, should be understood todescribe a relative relationship between the components or a spatialorientation of aspects of such components, respectively, as thecomponents described herein may be oriented in any desired direction.When used to describe a range of dimensions or other characteristics(e.g., time, pressure, temperature, length, width, etc.) of an element,operations, and/or conditions, the phrase “between X and Y” represents arange that includes X and Y.

Other features and advantages of the disclosure will be apparent fromthe following description and the claims.

One embodiment is a perception system for an autonomous vehicle. Theperception system may receive full resolution information from aplurality of sensors and imagers of the autonomous vehicle, as well asthe autonomous vehicle's intent and current state from the autonomousvehicle's planner and control system. Based on the received information,a perception filter of the perception system may dynamically scale andcrop the image data from the imagers based on what information is themost important, or relevant, given the autonomous vehicle's currentstate and intent. For example, if the autonomous vehicle is traveling ona highway at high speed, straight ahead at a long range to the horizonis likely the most important area on which to focus, or region ofinterest (“ROI”). In such a situation, high-resolution crops of theimage data comprising the ROI, rather than the entire image from theimager(s), may be provided to a perception module to perceive theautonomous vehicle's surroundings. In contrast, when driving at lowspeed on in a city, the surroundings all around the autonomous vehicleare important and may comprise the ROI. In such a situation, lowresolution images and sensor data from all around the vehicle may beprovided to a perception module to perceive the autonomous vehicle'srelevant surroundings.

Additionally, the perception filter of the perception system may scalethe compute resources and resolution for the particular sensor based onthe state and intent of the autonomous vehicle. In particular, given thenature of autonomous vehicle driving, many systems often compete forlimited resources (e.g., CPU and GPU resources). This resourceallocation can be changed at real-time depending upon the relativeimportance of the resource data as dictated by the ROI(s) based onautonomous vehicle state and intent. For example, if the autonomousvehicle is changing lanes at highway speed, the ROI will be in thedirection of the intended lane change (e.g., left or right) at asubstantial distance. In accordance with features of embodimentsdescribed herein, the perception system may allocate greater computeresources to the camera and/or other sensor(s) directed to theidentified ROI and simultaneously reduce compute resources of othersensors (i.e., sensors directed to regions other than the ROI) e.g., byreducing the resolution of those sensors. As a result, overall systemlatency is reduced while operational quality remains high.

As shown in FIG. 1, a system 100 implementing intent-based dynamicchange of resolution, region of interest (ROI), and compute resources ofa perception system includes an autonomous vehicle 110 including apassenger interface 120, a vehicle coordinator 130, and/or a remoteexpert interface 140. In certain embodiments, the remote expertinterface 140 allows a non-passenger entity to set and/or modify thebehavior settings of the autonomous vehicle 110. The non-passengerentity may be different from the vehicle coordinator 130, which may be aserver.

The system 100 functions to enable an autonomous vehicle 110 to modifyand/or set a driving behavior in response to parameters set by vehiclepassengers (e.g., via the passenger interface 120) and/or otherinterested parties (e.g., via the vehicle coordinator 130 or remoteexpert interface 140). Driving behavior of an autonomous vehicle may bemodified according to explicit input or feedback (e.g., a passengerspecifying a maximum speed or a relative comfort level), implicit inputor feedback (e.g., a passenger's heart rate), or any other suitable dataor manner of communicating driving behavior preferences.

The autonomous vehicle 110 is preferably a fully autonomous automobile,but may additionally or alternatively be any semi-autonomous or fullyautonomous vehicle; e.g., a boat, an unmanned aerial vehicle, adriverless car, etc. Additionally, or alternatively, the autonomousvehicles may be vehicles that switch between a semi-autonomous state anda fully autonomous state and thus, some autonomous vehicles may haveattributes of both a semi-autonomous vehicle and a fully autonomousvehicle depending on the state of the vehicle.

The autonomous vehicle 110 preferably includes a throttle interface thatcontrols an engine throttle, motor speed (e.g., rotational speed ofelectric motor), or any other movement-enabling mechanism; a brakeinterface that controls brakes of the autonomous vehicle (or any othermovement-retarding mechanism); and a steering interface that controlssteering of the autonomous vehicle (e.g., by changing the angle ofwheels of the autonomous vehicle). The autonomous vehicle 110 mayadditionally or alternatively include interfaces for control of anyother vehicle functions; e.g., windshield wipers, headlights, turnindicators, air conditioning, etc.

In addition, the autonomous vehicle 110 preferably includes an onboardcomputer 145 and a sensor suite 150 (e.g., computer vision (“CV”)system, LIDAR, RADAR, wheel speed sensors, GPS, cameras, etc.). Theonboard computer 145 functions to control the autonomous vehicle 110 andprocesses sensed data from the sensor suite 150 and/or other sensors inorder to determine the state of the autonomous vehicle 110. Based uponthe vehicle state and programmed instructions, the onboard computer 145preferably modifies or controls driving behavior of the autonomousvehicle 110.

Driving behavior may include any information relating to how anautonomous vehicle drives (e.g., actuates brakes, accelerator, steering)given a set of instructions (e.g., a route or plan). Driving behaviormay include a description of a controlled operation and movement of anautonomous vehicle and the manner in which the autonomous vehicleapplies traffic rules during one or more driving sessions. Drivingbehavior may additionally or alternatively include any information abouthow an autonomous vehicle calculates routes (e.g., prioritizing fastesttime vs. shortest distance), other autonomous vehicle actuation behavior(e.g., actuation of lights, windshield wipers, traction controlsettings, etc.) and/or how an autonomous vehicle responds toenvironmental stimulus (e.g., how an autonomous vehicle behaves if it israining, or if an animal jumps in front of the vehicle). Some examplesof elements that may contribute to driving behavior include accelerationconstraints, deceleration constraints, speed constraints, steeringconstraints, suspension settings, routing preferences (e.g., scenicroutes, faster routes, no highways), lighting preferences, “legalambiguity” conduct (e.g., in a solid-green left turn situation, whethera vehicle pulls out into the intersection or waits at the intersectionline), action profiles (e.g., how a vehicle turns, changes lanes, orperforms a driving maneuver), and action frequency constraints (e.g.,how often a vehicle changes lanes).

The onboard computer 145 functions to control the operations andfunctionality of the autonomous vehicles 110 and processes sensed datafrom the sensor suite 150 and/or other sensors in order to determinestates of the autonomous vehicles no. Based upon the vehicle state andprogrammed instructions, the onboard computer 145 preferably modifies orcontrols behavior of autonomous vehicles 110. The onboard computer 145is preferably a general-purpose computer adapted for I/O communicationwith vehicle control systems and sensor systems but may additionally oralternatively be any suitable computing device. The onboard computer 145is preferably connected to the Internet via a wireless connection (e.g.,via a cellular data connection). Additionally, or alternatively, theonboard computer 145 may be coupled to any number of wireless or wiredcommunication systems.

The sensor suite 150 preferably includes localization and drivingsensors; e.g., photodetectors, cameras, RADAR, SONAR, LIDAR, GPS,inertial measurement units (IMUS), accelerometers, microphones, straingauges, pressure monitors, barometers, thermometers, altimeters, etc.

Referring now to FIG. 2, illustrated therein is a perception system 200for an autonomous vehicle, such as the autonomous vehicle 110. Part orall of the perception system 200 may be implemented as a sensor suite,such as the sensor suite 150, and/or an onboard computer, such asonboard computer 145. As shown in FIG. 2, the perception system includesa perception filter 205, which comprises hardware and/or software forprocessing information and data from a variety of sources, including butnot limited to cameras 210, LIDARS 215, RADARs 220, vehicle state 225,vehicle intent 230 (which may be based on/derived from the plannedroute), and/or world map information 235. As will be described ingreater detail below, the perception filter 205 processes the receivedinformation/data to filter and scale the information and providesprocessed/filtered information to a perception module 240, which makesinferences about properties of the physical environment of theautonomous vehicle based on data provided to it (e.g., from theperception filter 205). In particular, the perception module 240 mayperform detection and segmentation on the information it receives.Additionally, and/or alternatively, the perception filter 205 processesthe received information/data and provides compute prioritizationinformation to a compute system 245, which allocates compute resourcesto devices of the sensor suite (e.g., sensor suite 150). The computesystem 245 is the main CPU/GPU compute resource in the vehicle.

Cameras 210 may be implemented using high-resolution imagers with fixedmounting and field of view. LIDARs 215 may be implemented using scanningLIDARs with dynamically configurable field of view that provides apoint-cloud of the region intended to scan. RADARs 220 may beimplemented using scanning RADARs with dynamically configurable field ofview. Vehicle state 225 includes the current position, velocity, andother state(s) of the vehicle. Vehicle intent 230 includes the intent ofthe vehicle, such as lane change, turning, etc. World map 235 is ahigh-definition map of the world, which includes semantics and heightinformation.

FIG. 3 is a flowchart of an example method 300 implemented by an exampleperception system according to some embodiments of the presentdisclosure, such as the perception system 200 of FIG. 2. In step 305,the perception system (e.g., the perception filter) determines theintent of the autonomous vehicle (e.g., using the planned route thereof)and the current state of the autonomous vehicle. In step 310, theperception system (e.g., the perception filter) crops and filters datareceived from imagers and sensors (e.g., cameras, LIDARs, and RADARs)according to an ROI determined based on the current state and intent ofthe autonomous vehicle and provides the cropped and filter data to aperception module of the perception system. In step 315, the perceptionmodule uses the cropped and filtered data to perceive the environment ofthe autonomous vehicle and take appropriate action based on theperceived environment, current state, and intent.

Additionally, and/or alternatively, in step 320, the perception system(e.g., the perception filter) determines compute resource priority basedon ROI and/or current state and intent of the autonomous vehicle andprovides compute resource priority instructions to a compute module ofthe perception system. In step 325, the compute module implements thecompute resource priority instructions by allocating resources to theimagers and sensors comprising the sensor suite in accordance with thepriority instructions.

It will be recognized that steps 310/315 may be implemented before,after, contemporaneously with, or in lieu of steps 320/325. Similarly,steps 320/325 may be executed before, after, contemporaneously with, orin lieu of steps 310/315. Upon completion of step 315 and/or step 325,in step 330, perception system (e.g., the perception filter) providesscanning instructions to the sensor suite.

Table 1 below provides a non-exhaustive list of example use cases forillustrating operation of embodiments of a perception system such asdescribed herein in accordance with the method 300.

TABLE 1 INSTRUCTIONS INSTRUCTIONS INSTRUCTIONS TO PERCEPTION TO COMPUTETO SENSOR EX. AV INTENT AV STATE MODULE MODULE SUITE 1 Drive Driving Usehigh resolution Prioritize the LIDAR and straight on straight in sensorcrop from compute RADAR densely highway at its lane front sensors fromresources for scans the small high speed region of frame front sensorsto FOV in front of without corresponding to the keep latency low,vehicle lane change road section for long- corresponding to rangedetection. Use ROI. Sparse scaled sensor scanning information fromeverywhere else. other sensors for low- range detection. 2 Make aDriving Use high-resolution Prioritize the LIDAR and lane changestraight in sensor crops from compute RADAR densely from left to itslane behind the vehicle for resources for rear scans the FOV in rightlane long-range detection. sensors to keep the area Use scaled sensorlatency low, corresponding to information from ROI. Sparse other sensorsfor low scanning range detection. everywhere else. 3 Make a Pointing Usehigh-resolution Prioritize the LIDAR and right turn straight sensorinformation compute RADAR densely forward from front and left ofresources for left scans FOV on left waiting to vehicle for long-rangeand front sensors and right make turn detection in those to keep latencycorresponding to regions. Use scaled low. ROls. Sparse sensorinformation scanning from other sensors everywhere else. for low-rangedetection. 4 Drive Driving Use scaled low- Apply uniform Uniformscanning straight in a straight in resolution compute everywhere.residential its lane information for low resources. area at low rangedetection from speed all sensors.

Examples 1-4 listed in Table 1 are illustrated in and will be describedin greater detail with reference to FIGS. 4A-4D. Referring to FIG. 4A,which illustrates example 1 from Table 1, a vehicle 400 is drivingstraight on a highway (relatively high speed) without planning to make alane change. The image/sensor information provided by the perceptionfilter to the perception module may include a high-resolution sensorcrop from a region of the image frame that corresponds to the roadsection for long-range detection. Additionally, scaled information fromother sensors may also be provided for low-range detection in otherregions. The compute module may be instructed to prioritize computeresources for forward sensors to keep system latency low. Moreover,LIDAR and RADAR devices are instructed to densely scan the small fieldof view (FOV) in front of the vehicle corresponding to the region ofinterest (ROI) and to sparsely scan FOV outside the ROI. In thisscenario, the ROI may be represented by an area 405.

Referring to FIG. 4B, which illustrates Example 2 from Table 1, avehicle 410 is driving on the highway (relatively high speed) andplanning to make a lane change from the left lane to the right lane. Theimage/sensor information provided by the perception filter to theperception module may include high-resolution sensor crops from behindthe vehicle for long-range detection. Additionally, scaled informationfrom other sensors may also be provided for low-range detection in otherregions. The compute module may be instructed to prioritize computeresources for rear sensors to keep system latency low. Moreover, LIDARand RADAR devices are instructed to densely scan the FOV in the areacorresponding to the ROI and to sparsely scan FOV outside the ROI. Inthis scenario, the ROI may be represented by an area 415.

Referring to FIG. 4C, which illustrates Example 3 from Table 1, avehicle 420 is planning to make a right turn at a residential(relatively low speed) intersection. The image/sensor informationprovided by the perception filter to the perception module may includehigh-resolution sensor information from the front and left of thevehicle for long range detection in those regions. Additionally, scaledsensor information from other sensors may also be provided for low-rangedetection in other regions. The compute module may be instructed toprioritize compute resources for front and left sensors to keep systemlatency low. Moreover, LIDAR and RADAR devices are instructed to denselyscan the FOV on the left and right corresponding to the ROIs and tosparsely scan everywhere else. In this scenario, the ROIs may berepresented by an area 425.

Finally, referring to FIG. 4D, which illustrates Example 4 from Table 1,a vehicle 430 is planning to continue driving straight on a residentialstreet (relatively low speed). The image/sensor information provided bythe perception filter to the perception module may include scaledlow-resolution information for low-range detection from all sensors. Thecompute module may be instructed to apply uniform compute resources toall sensors. Moreover, the sensors (e.g., LIDAR and RADAR devices) areinstructed to apply uniform scanning everywhere. In this scenario, theROI may be represented by an area 435.

As described herein, one aspect of the present technology may includethe gathering and use of data available from various sources to improvequality and experience. The present disclosure contemplates that in someinstances, this gathered data may include personal information. Thepresent disclosure contemplates that the entities involved with suchpersonal information respect and value privacy policies and practices.

It is to be understood that not necessarily all objects or advantagesmay be achieved in accordance with any particular embodiment describedherein. Thus, for example, those skilled in the art will recognize thatcertain embodiments may be configured to operate in a manner thatachieves or optimizes one advantage or group of advantages as taughtherein without necessarily achieving other objects or advantages as maybe taught or suggested herein.

In one example embodiment, any number of electrical circuits of theFIGS. may be implemented on a board of an associated electronic device.The board can be a general circuit board that can hold variouscomponents of the internal electronic system of the electronic deviceand, further, provide connectors for other peripherals. Morespecifically, the board can provide the electrical connections by whichthe other components of the system can communicate electrically. Anysuitable processors (inclusive of digital signal processors,microprocessors, supporting chipsets, etc.), computer-readablenon-transitory memory elements, etc. can be suitably coupled to theboard based on particular configuration needs, processing demands,computer designs, etc. Other components such as external storage,additional sensors, controllers for audio/video display, and peripheraldevices may be attached to the board as plug-in cards, via cables, orintegrated into the board itself. In various embodiments, thefunctionalities described herein may be implemented in emulation form assoftware or firmware running within one or more configurable (e.g.,programmable) elements arranged in a structure that supports thesefunctions. The software or firmware providing the emulation may beprovided on non-transitory computer-readable storage medium comprisinginstructions to allow a processor to carry out those functionalities.

In another example embodiment, the electrical circuits of the FIGS. maybe implemented as stand-alone modules (e.g., a device with associatedcomponents and circuitry configured to perform a specific application orfunction) or implemented as plug-in modules into application specifichardware of electronic devices. Note that particular embodiments of thepresent disclosure may be readily included in a system on chip (SOC)package, either in part, or in whole. An SOC represents an IC thatintegrates components of a computer or other electronic system into asingle chip. It may contain digital, analog, mixed-signal, and oftenradio frequency functions: all of which may be provided on a single chipsubstrate. Other embodiments may include a multi-chip-module (MCM), witha plurality of separate ICs located within a single electronic packageand configured to interact closely with each other through theelectronic package. In various other embodiments, the digital filtersmay be implemented in one or more silicon cores in Application SpecificIntegrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), andother semiconductor chips.

It is also imperative to note that all of the specifications,dimensions, and relationships outlined herein (e.g., the number ofprocessors, logic operations, etc.) have only been offered for purposesof example and teaching only. Such information may be variedconsiderably without departing from the spirit of the presentdisclosure, or the scope of the appended claims. The specificationsapply only to one non-limiting example and, accordingly, they should beconstrued as such. In the foregoing description, example embodimentshave been described with reference to particular arrangements ofcomponents. Various modifications and changes may be made to suchembodiments without departing from the scope of the appended claims. Thedescription and drawings are, accordingly, to be regarded in anillustrative rather than in a restrictive sense.

Note that with the numerous examples provided herein, interaction may bedescribed in terms of two, three, four, or more electrical components.However, this has been done for purposes of clarity and example only. Itshould be appreciated that the system can be consolidated in anysuitable manner. Along similar design alternatives, any of theillustrated components, modules, and elements of the FIGS. may becombined in various possible configurations, all of which are clearlywithin the broad scope of this Specification. In certain cases, it maybe easier to describe one or more of the functionalities of a given setof flows by only referencing a limited number of electrical elements. Itshould be appreciated that the electrical circuits of the FIGS. and itsteachings are readily scalable and can accommodate a large number ofcomponents, as well as more complicated/sophisticated arrangements andconfigurations. Accordingly, the examples provided should not limit thescope or inhibit the broad teachings of the electrical circuits aspotentially applied to a myriad of other architectures.

Note that in this Specification, references to various features (e.g.,elements, structures, modules, components, steps, operations,characteristics, etc.) included in “one embodiment”, “exampleembodiment”, “an embodiment”, “another embodiment”, “some embodiments”,“various embodiments”, “other embodiments”, “alternative embodiment”,and the like are intended to mean that any such features are included inone or more embodiments of the present disclosure, but may or may notnecessarily be combined in the same embodiments.

It is also important to note that the functions related to contactlesscurrent measurement using magnetic sensors, e.g. those summarized in theone or more processes shown in FIGS., illustrate only some of thepossible functions that may be executed by, or within, the currentmeasurement systems illustrated in the FIGS. Some of these operationsmay be deleted or removed where appropriate, or these operations may bemodified or changed considerably without departing from the scope of thepresent disclosure. In addition, the timing of these operations may bealtered considerably. The preceding operational flows have been offeredfor purposes of example and discussion. Substantial flexibility isprovided by embodiments described herein in that any suitablearrangements, chronologies, configurations, and timing mechanisms may beprovided without departing from the teachings of the present disclosure.

Numerous other changes, substitutions, variations, alterations, andmodifications may be ascertained to one skilled in the art and it isintended that the present disclosure encompass all such changes,substitutions, variations, alterations, and modifications as fallingwithin the scope of the appended claims. Note that all optional featuresof the apparatus described above may also be implemented with respect tothe method or process described herein and specifics in the examples maybe used anywhere in one or more embodiments.

In order to assist the United States Patent and Trademark Office (USPTO)and, additionally, any readers of any patent issued on this applicationin interpreting the claims appended hereto, Applicant wishes to notethat the Applicant: (a) does not intend any of the appended claims toinvoke paragraph (f) of 35 U.S.C. Section 112 as it exists on the dateof the filing hereof unless the words “means for” or “step for” arespecifically used in the particular claims; and (b) does not intend, byany statement in the Specification, to limit this disclosure in any waythat is not otherwise reflected in the appended claims.

What is claimed is:
 1. A perception system for a vehicle, the perceptionsystem comprising: a plurality of imaging devices associated with thevehicle; a perception filter for determining a region of interest(“ROI”) for the vehicle based on an intent of the vehicle and a currentstate of the vehicle, wherein the ROI is used to select images of anenvironment of the vehicle produced by the imaging devices and whereinthe perception filter receives and processes the images produced by theimaging devices; and a perception module for receiving the processedimages from the perception filter and perceiving the environment of thevehicle based at least in part on the received images.
 2. The perceptionsystem of claim 1, wherein the ROI is used by the perception filter tocrop the images produced by the imaging devices.
 3. The perceptionsystem of claim 1, wherein the ROI is used by the perception filter todetermine a resolution of the images produced by the imaging devices. 4.The perception system of claim 1, wherein the perception filter furtherprovides scanning instructions to at least one of the imaging devicesbased on the images produced by the imaging devices.
 5. The perceptionsystem of claim 1, wherein the perception filter crops and filters theimages produced by the imaging devices based on an intent of the vehicleand a current state of the vehicle.
 6. The perception system of claim 5,wherein the perception module receives at least one of the cropped andfiltered images from the perception filter and perceives the environmentof the vehicle based at least in part on the received at least one ofthe cropped and filtered images.
 7. The perception system of claim 1,wherein the perception filter further uses the vehicle's intent andvehicle's current state to determine compute resource priorityinstructions.
 8. The perception system of claim 7 further comprising acompute module for receiving the compute resource priority instructionsfrom the perception module and allocating compute resources among theimaging devices in accordance with the compute resource priorityinstructions.
 9. The perception system of claim 1, wherein the vehiclecomprises an autonomous vehicle.
 10. The perception system of claim 1,wherein the imaging devices comprise at least one of a camera, a LIDARdevice, and a RADAR device.
 11. An autonomous vehicle (“AV”) comprising:an onboard computer; a sensor suite comprising a plurality of imagingdevices for producing images of an environment of the AV; and aperception system including: a perception filter for determining aregion of interest (“ROI”) for the vehicle based on an intent of thevehicle and a current state of the vehicle, wherein the ROI is used toselect images of an environment of the vehicle produced by the imagingdevices and wherein the perception filter receives and processes theimages produced by the imaging devices; and a perception module forreceiving the processed images from the perception filter and perceivingthe environment of the vehicle based at least in part on the receivedimages.
 12. The AV of claim 11, wherein the ROI is used by theperception filter to crop the images produced by the imaging devices.13. The AV of claim 11, wherein the ROI is used by the perception filterto determine a resolution of the images produced by the imaging devices.14. The AV of claim 11, wherein the perception filter further providesscanning instructions to at least one of the imaging devices based onthe images produced by the imaging devices.
 15. The AV of claim 11,wherein the perception filter crops and filters the images produced bythe imaging devices based on an intent of the vehicle and a currentstate of the vehicle and the perception module receives at least one ofthe cropped and filtered images from the perception filter and perceivesthe environment of the vehicle based at least in part on the received atleast one of the cropped and filtered images.
 16. The AV of claim 11,wherein the perception filter further uses the vehicle's intent andvehicle's current state to determine compute resource priorityinstructions, the perception system further comprising a compute modulefor receiving the compute resource priority instructions from theperception module and allocating compute resources among the imagingdevices in accordance with the compute resource priority instructions.17. A method comprising: deploying a plurality of imaging devices forproducing images of an environment of a vehicle; determining by a regionof interest (“ROI”) for the vehicle based on an intent of the vehicleand a current state of the vehicle, wherein the ROI is used to selectimages of an environment of the vehicle produced by the imaging devicesand wherein the perception filter receives and processes the imagesproduced by the imaging devices; and receiving by a perception modulethe processed images from the perception filter and perceiving theenvironment of the vehicle based at least in part on the receivedimages.
 18. The method of claim 17, wherein the ROI is used by theperception filter to crop the images produced by the imaging devices.19. The method of claim 17, wherein the ROI is used by the perceptionfilter to determine a resolution of the images produced by the imagingdevices.
 20. The method of claim 17, wherein the perception filterfurther provides scanning instructions to at least one of the imagingdevices based on the images produced by the imaging devices.